!48629 modify format

Merge pull request !48629 from 俞涵/code_docs_1110

docs/api/api_python/ops/mindspore.ops.RaggedRange.rst

@@ -16,7 +16,7 @@ mindspore.ops.RaggedRange
     输出：
         - **rt_nested_splits** (Tensor) - 返回RagdTensor的嵌套拆分Tensor，数据类型类型为 `Tsplits` 。shape等于输入 `starts` 的shape加1。
         - **rt_dense_values** (Tensor) - 返回RagdTensor的密集值Tensor，其数据类型与输入 `starts` 相同。设输入 `starts、` `limits` 和 `delta` 的大小为i。
-          - 如果 `starts` 、 `limits` 和 `delta` 的数据类型为int32或int64，则输出 `rt_dense_values` 的shape等于 :math:`sum(abs(limits[i] - starts[i]) + abs(deltas[i]) - 1) / abs(deltas[i]))` 。
+          - 如果 `starts` 、 `limits` 和 `delta` 的数据类型为int32或int64，则输出 `rt_dense_values` 的shape等于 :math:`sum(abs(limits[i] - starts[i]) + abs(deltas[i] - 1) / abs(deltas[i]))` 。
           - 如果 `starts` 、 `limits` 和 `delta` 的数据类型为float32或者float64，则输出 `rt_dense_values` 的shape等于 :math:`sum(ceil(abs((limits[i] - starts[i]) / deltas[i]))` 。
 
     异常：

mindspore/python/mindspore/common/mutable.py

@@ -76,7 +76,7 @@ def mutable(input_data, dynamic_len=False):
     the length of the tuple or list is different for each run, it does not need to be re-compiled.
 
     Args:
-        input_data (Union[int, float, Tensor, tuple, list, dict): The input data to be made mutable. If
+        input_data (Union[int, float, Tensor, tuple, list, dict]): The input data to be made mutable. If
             'input_data' is list/tuple/dict, the type of each element should also in the valid types.
         dynamic_len (bool): Whether to set the whole sequence to be dynamic length. In graph compilation, if
             `dynamic_len` is True, the `input_data` must be list or tuple and the elements of `input_data` must have

mindspore/python/mindspore/dataset/vision/transforms.py

@@ -1591,7 +1591,7 @@ class Pad(ImageTensorOperation, PyTensorOperation):
               value of edge.
 
     Raises:
-        TypeError: If `padding` is not of type int or Sequence[int, int], Sequence[int, int, int, int]].
+        TypeError: If `padding` is not of type int or Sequence[int, int], Sequence[int, int, int, int].
         TypeError: If `fill_value` is not of type int or tuple[int].
         TypeError: If `padding_mode` is not of type :class:`mindspore.dataset.vision.Border` .
         ValueError: If `padding` is negative.

mindspore/python/mindspore/nn/layer/pooling.py

@@ -1415,8 +1415,8 @@ class MaxUnpool1d(Cell):
 
     MaxUnpool1d takes in as input the output of MaxPool1d including the indices of the maximal values
     and computes a partial inverse in which all non-maximal values are set to zero. Typically the input
-    is of shape :math:`(N, C, H_{in})` or :math:`(C, H_{in})`, and the output is of shape :math:`(N, C, H_{out}`
+    is of shape :math:`(N, C, H_{in})` or :math:`(C, H_{in})`, and the output is of shape :math:`(N, C, H_{out})`
-    or :math:`(C, H_{out}`. The operation is as follows.
+    or :math:`(C, H_{out})`. The operation is as follows.
 
     .. math::
         \begin{array}{ll} \\

mindspore/python/mindspore/nn/transformer/layers.py

@@ -575,7 +575,7 @@ class FixedSparseAttention(nn.Cell):
           queries to query the context.
         - **k** (Tensor) - Tensor key ( `mstype.fp16` [batch_size, seq_length, hidden_size]): Sequence of
           queries to query the context.
-        - **v** (Tensor) - Tensor value  `mstype.fp16` [batch size, sequence length, Embedding Size]):
+        - **v** (Tensor) - Tensor value ( `mstype.fp16` [batch size, sequence length, Embedding Size]):
           Sequence of queries to query the context.
         - **attention_mask** (Tensor) - Float Tensor the mask of ( `mstype.fp32`,  `mstype.fp16`
           [batch_size, seq_length, seq_length]): Lower triangular matrix to pass masked information.

mindspore/python/mindspore/ops/function/nn_func.py

@@ -1939,7 +1939,7 @@ def interpolate(x, size=None, scale_factor=None, mode="nearest", align_corners=N
         x (Tensor): Tensor to be resized.
             Input tensor must be a 3-D, 4-D, or 5-D tensor with shape
             `(batch, channels, [optional depth], [optional height], width)`, with data type of float.
-        size (Union[int, tuple[int], list[int]], optional)): The target size.
+        size (Union[int, tuple[int], list[int]], optional): The target size.
             If size is a tuple or list, size must have the same dimensions as x.
             One and only one of size and scale_factor can be set to None. Default: None.
         scale_factor (Union[float, tuple[float], list[float]], optional): The scale factor of new size of the tensor.
@@ -1996,7 +1996,7 @@ def interpolate(x, size=None, scale_factor=None, mode="nearest", align_corners=N
     - `×` indicates that this parameter is not currently supported.
     - `√` indicates that this parameter is supported.
 
-    Outputs:
+    Returns:
         Tensor, resized, whose dimensions and dtype are the same as `x`.
 
     Raises:

mindspore/python/mindspore/ops/operations/math_ops.py

@@ -6644,7 +6644,7 @@ class RaggedRange(Primitive):
 
           - if type of the input `starts`, input `limits` and input `deltas`
             are int32 or int64, shape of the output `rt_dense_values` is equal to
-            sum(abs(limits[i] - starts[i]) + abs(deltas[i]) - 1) / abs(deltas[i])),
+            sum(abs(limits[i] - starts[i]) + abs(deltas[i] - 1) / abs(deltas[i])),
           - if type of the input `starts`, input `limits` and input `deltas`
             are float32 or float64, shape of the output `rt_dense_values` is equal to
             sum(ceil(abs((limits[i] - starts[i]) / deltas[i]))).

mindspore/python/mindspore/train/callback/_landscape.py

@@ -282,7 +282,7 @@ class SummaryLandscape:
                   For example, in image dataset, You can set num_samples is 2048,
                   which means that 2048 images are used to create loss landscape.
                   Default: 2048.
-                - intervals (List[List[int]): Specifies the interval
+                - intervals (List[List[int]]): Specifies the interval
                   in which the loss landscape. For example: If the user wants to
                   create loss landscape of two training processes, they are 1-5 epoch
                   and 6-10 epoch respectively. They can set [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]].